Expression of cell type markers in fetal neocortex Polioudakis 2019 dataset

We will use the Polioudakis 2019 data to check UMI counts in each cell type for many marker genes including HOPX (oRG), EOMES (IP), STMN2 (Neurons), PDGFRA (OPCs):

markerGenes = c('HOPX', 'EOMES', 'STMN2', 'PDGFRA', 'TBR1', 'PAX6', 'TNC', 'SOX2', 'FAM107A', 'BCL11B', 'SATB2')
setwd('/home/jovyan/FetalBrainMarkers/')
load('../data/fetalBrain/Polioudakis/raw_counts_mat.rdata')
metadata = read.delim('../data/fetalBrain/Polioudakis/cell_metadata.csv', sep = ',')
raw_counts_mat = raw_counts_mat[,metadata$Cell]

df = data.frame(cluster = metadata$Cluster, subcluster = metadata$Subcluster, HOPX = raw_counts_mat['HOPX', ], EOMES = raw_counts_mat['EOMES', ], STMN2 = raw_counts_mat['STMN2', ], PDGFRA = raw_counts_mat['PDGFRA',], TBR1 = raw_counts_mat['TBR1', ], PAX6 = raw_counts_mat['PAX6', ], TNC = raw_counts_mat['TNC', ], SOX2 = raw_counts_mat['SOX2', ], FAM107A = raw_counts_mat['FAM107A',], BCL11B = raw_counts_mat['BCL11B', ], SATB2 = raw_counts_mat['SATB2', ])
for (i in 1:length(markerGenes)){
  print(ggplot(df, aes(x=cluster, y=get(markerGenes[i]))) + geom_jitter(size = 0.01) + 
  stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="pointrange", color="red") + 
  ylab(paste(markerGenes[i], 'UMI Count', sep = ' ')) +
  xlab(NULL) + 
  ggtitle(paste(markerGenes[i], 'Expression across Celltypes', sep = ' ')) + 
  theme(axis.text=element_text(size=12)))
}